Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

We combine neural rendering with multi-modal image and text representations to synthesize
diverse 3D objects solely from natural language descriptions. Our method, Dream Fields …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Training deep neural networks requires gradient estimation from data batches to update
parameters. Gradients per parameter are averaged over a set of data and this has been …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Understanding to what extent neural networks memorize training data is an intriguing
question with practical and theoretical implications. In this paper we show that in some …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …